Natural Language Processing in the Era of Big Data - Withdrawn

Recent technological revolutions have reshaped the landscape of Natural Language Processing, driven by the convergence of multi-modality data in a large scale, transformer‑based representation techniques and advances in artificial intelligence. As many of us have observed, these developments have positioned NLP as a deeply interdisciplinary field, bridging data science, linguistics, cognitive science and digital humanities. Yet, despite the impressive success of large language models, our collective experience reminds us that progress in NLP remains fundamentally grounded in data quality and linguistic insight—robustness, interpretability and generalizability cannot be achieved by scale alone. The growing emphasis on cognition‑ and perception‑aware representations, embodied grounding and linked data infrastructures underscore why this research area continues to matter: it enables us to build language technologies that are not only powerful but also humanistic, reliable, explainable and socially responsible.

We are pleased to invite you to contribute to this Special Issue, which aims to bring together high‑quality research on Natural Language Processing in the era of big data, in line with the journal’s emphasis on data‑centric methodologies and analytical rigor. The Special Issue focuses on how large‑scale datasets, representation learning and artificial intelligence can be effectively integrated with linguistic and cognitive insights to advance robust, interpretable and responsible NLP systems. Its scope is intentionally balanced to ensure thematic coherence while accommodating interdisciplinary perspectives.

Indicative topics include, but are not limited to, (1) data quality, curation and annotation for NLP; (2) corpus linguistics and data‑driven language analysis; (3) representation learning and large language models with linguistic grounding; (4) cognition‑, perception‑ and embodiment‑aware NLP; and (5) linked open data and knowledge‑enhanced language technologies. We welcome original research articles, review papers and data‑centric contributions that offer methodological innovation, empirical insights or reusable resources aligned with the scope of the Special Issue.

Our goal is to assemble a collection of at least ten high‑quality articles. Upon reaching this target, the Special Issue may be published in book form, providing enhanced visibility and long‑term impacts for the contributing works.

We look forward to receiving your contributions.

Prof. Dr. Chu Ren Huang
Dr. Mingyu Wan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Linguistically enriched encodings
• embodied shared knowledge
• linked open data
• NLP enhancement
• perception–cognition–experiential AI